In general, a mobile service robot performs various basic operations in an indoor environment. For example, the mobile service robot recognizes an object or a situation, navigates to a predetermined destination, grips a predetermined object, collects information about a predetermined object, and stores collected information in a database. The mobile service robot must have capability of performing operations requiring high level technologies such as simultaneous localization and map building (SLAM), self-modeling, and categorization. There have been many researches in progress for individually developing each of high-intelligent functions of a mobile service robot.
However, if a mobile service robot is built by simply combining such individually developed functions together, compatibility between functional modules may deteriorate because a mobile service robot may have different data formats and different memories according to each function. Also, large amount of duplicated information may be generated because each of robots has similar information respectively. It is inefficient in a view of data operability. For example, when a mobile service robot receives a mission of fetching a green tea in a refrigerator at a kitchen, the mobile service robot performs sequence of operations such as navigation, recognition, and manipulation. If green tea information for recognition is different from green tea information for manipulation, and if refrigerator information for navigation is stored separately from refrigerator information for recognition, information redundancy may occur because information is not integrally managed. If such waste continues, inefficiency continuously increases in managing memory spaces and in compatibility between functional modules. If a robot collects the same information about a refrigerator, a green tea, and a kitchen through sensing and perception although the other robot already has the same sensed or perception information of the refrigerator, the green tea, and the kitchen, the robots may waste resources due to spatial/temporal redundancy.
Also, if service robots were individually developed according to each of functions, each of the service robots has limited capability to provide low level services. For example, if a mobile service robot does not have high level reasoning information or context information for a space or an object although the mobile service robot has superior navigation capability using a high level reasoning technique such as SLAM, the mobile service robot may have low capability of learning knowledge about environments and objects and understanding relations thereof. Then, the robot cannot perform operation based on understanding about a space and an object in an indoor space. For example, the robot cannot perform an operation for fetching a green tea in a refrigerator at a kitchen, an operation for bring a cup of juice on a table at a kitchen to a master sat on sofa at a living room. In order to perform such a high level operation or service, it is necessary to have a knowledge system or structure for an object and a space in addition to capability for accurately navigating to a destination and for building a map. That is, a robot must understand a space or an object.
Furthermore, it is more necessary to share information about a space or an object with robots in an indoor space where a plurality of service robots operate together. That is, information collected by a service robot must be shared with the other service robots in the same environment and the context must be reflected to services provided by the other service robots. Particularly, if a plurality of robots having different recognition/reasoning levels are in the same environment, it is necessary to process and transform spatial and object information according to a recognition/reasoning level of each robot and to share the processed and transformed information.
Therefore, there has been a demand for developing a system for integrally managing information and sharing the information with various service robots. Related researches have been partially made, recently.
A geographic information system (GIS) is one of methods for effectively representing and managing geometric/spatial information. That is, the GIS is one of technologies for helping a user to make intelligent decision. The GIS is also a system that maps data having properties to multiple layers. In general, the GIS describes only information suitable to a purpose in detail. The general GIS data is not suitable to a service robot for performing precise operations in home environment because the GIS data is limited to an outdoor space or a ground. Also, an indoor environment has been modeled using computer aided design (CAD) many times and numerous commercial products thereof have been introduced. The indoor environment modeling has been standardized through Industrial Foundation Classes (IFC). However, the requirements of a home service robot are not satisfied by the CAD because the CAD provides only geometric building structure information.
In a web service field, many tries have been made for integrally managing information about individuals who are an object of providing a service by a home service robot. That is, the behavior patterns of individuals are observed, ontology is created based on the observation data, and new information about an individual is created through reasoning using the ontology. It is expected that such approach helps a robot to easily understand requirements of human rather than helping a robot to perform a given mission.
Information system approach for physical data or raw data has been used in fields that collect data limited to characteristics of sensors or fields that collect raw data processed in a low level. Such collected information is not suitable for a service robot because the reusability thereof is low or the collected information cannot be linked with abstract knowledge.
Among researches about human voice in a view of Human-Robot Interaction (HRI), an aurora project was made for linking raw data with high level abstract knowledge. In the aurora project, a central server stores vocabularies of human voice and is shared for analyzing preprocessed data and handling errors. However, an original purpose of distributed sound recognition is to reduce a bottleneck situation caused by lack of computational resources in a terminal.
Also, Orca was introduced as a system for sharing software components, not as a system for sharing data with robots. The object of the Orca is to effectively and continuously reuse components by simplifying general interfaces between components and making use of components easy. The Orca is realized not only through software component design but also through effective management of component repository. However, the Orca is middleware approach for binding connection of components with repositories as one framework.
Furthermore, a method for representing information about a space and objects recognized in a soccer game played with four-foot robots and storing the information was introduced. However, this method has shortcoming that robots exchange information only through task share using a token [reference 1].
Moreover, another study introduced a method for task based information generation and robot learning. Robots operating in home or offices must consider various environments and movements to perform a given mission. However, it is impossible that a robot perfectly builds knowledge about environments such as home and office in advance. Therefore, this study introduced new Teaching Framework as Task Model-Based Interactive Teaching [reference 2].
In addition, various approaches were introduced for systemizing data, information, and knowledge and grafting the systemized data, information, and knowledge to robots or automation equipment. However, it is required to newly develop an information processing system that can satisfy requirements of a service robot.    [reference 1] Farinelli, A. Iocchi, L. Nardi, D, and Ziparo, V. A. ‘Assignment of dynamically perceived tasks by token passing in multirobot systems, Proceedings of the IEEE, Special issue on Multi-Robot Systems, 2006’    [reference 2] Jun Miura, Koji Iwase, and Yoshiaki Shirari ‘Interactive Teaching of a Mobile Robot, In Proceeding of 2005 Int. Conf. on Robotic and Automation, pp. 3389-3394, 2005’